Multi-Resolution Discrete Cosine Transform Fusion Technique Face Recognition Model
This work presents a Multi-Resolution Discrete Cosine Transform (MDCT) fusion technique Fusion Feature-Level Face Recognition Model (FFLFRM) comprising face detection, feature extraction, feature fusion, and face classification. It detects core facial characteristics as well as local and global feat...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Online Access: | https://www.mdpi.com/2306-5729/7/6/80 |
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| author | Bader M. AlFawwaz Atallah AL-Shatnawi Faisal Al-Saqqar Mohammad Nusir |
| author_facet | Bader M. AlFawwaz Atallah AL-Shatnawi Faisal Al-Saqqar Mohammad Nusir |
| author_sort | Bader M. AlFawwaz |
| collection | DOAJ |
| description | This work presents a Multi-Resolution Discrete Cosine Transform (MDCT) fusion technique Fusion Feature-Level Face Recognition Model (FFLFRM) comprising face detection, feature extraction, feature fusion, and face classification. It detects core facial characteristics as well as local and global features utilizing Local Binary Pattern (LBP) and Principal Component Analysis (PCA) extraction. MDCT fusion technique was applied, followed by Artificial Neural Network (ANN) classification. Model testing used 10,000 faces derived from the Olivetti Research Laboratory (ORL) library. Model performance was evaluated in comparison with three state-of-the-art models depending on Frequency Partition (FP), Laplacian Pyramid (LP) and Covariance Intersection (CI) fusion techniques, in terms of image features (low-resolution issues and occlusion) and facial characteristics (pose, and expression per se and in relation to illumination). The MDCT-based model yielded promising recognition results, with a 97.70% accuracy demonstrating effectiveness and robustness for challenges. Furthermore, this work proved that the MDCT method used by the proposed FFLFRM is simpler, faster, and more accurate than the Discrete Fourier Transform (DFT), Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT). As well as that it is an effective method for facial real-life applications. |
| first_indexed | 2024-03-10T00:02:05Z |
| format | Article |
| id | doaj.art-2be191eac8db4f458c229d7ed4c0b6d9 |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-03-10T00:02:05Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-2be191eac8db4f458c229d7ed4c0b6d92023-11-23T16:14:51ZengMDPI AGData2306-57292022-06-01768010.3390/data7060080Multi-Resolution Discrete Cosine Transform Fusion Technique Face Recognition ModelBader M. AlFawwaz0Atallah AL-Shatnawi1Faisal Al-Saqqar2Mohammad Nusir3Department of Information Systems, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq 25113, JordanDepartment of Information Systems, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq 25113, JordanDepartment of Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq 25113, JordanCBM Integrated Software Inc. (CBMIS), San Diego, CA 92101, USAThis work presents a Multi-Resolution Discrete Cosine Transform (MDCT) fusion technique Fusion Feature-Level Face Recognition Model (FFLFRM) comprising face detection, feature extraction, feature fusion, and face classification. It detects core facial characteristics as well as local and global features utilizing Local Binary Pattern (LBP) and Principal Component Analysis (PCA) extraction. MDCT fusion technique was applied, followed by Artificial Neural Network (ANN) classification. Model testing used 10,000 faces derived from the Olivetti Research Laboratory (ORL) library. Model performance was evaluated in comparison with three state-of-the-art models depending on Frequency Partition (FP), Laplacian Pyramid (LP) and Covariance Intersection (CI) fusion techniques, in terms of image features (low-resolution issues and occlusion) and facial characteristics (pose, and expression per se and in relation to illumination). The MDCT-based model yielded promising recognition results, with a 97.70% accuracy demonstrating effectiveness and robustness for challenges. Furthermore, this work proved that the MDCT method used by the proposed FFLFRM is simpler, faster, and more accurate than the Discrete Fourier Transform (DFT), Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT). As well as that it is an effective method for facial real-life applications.https://www.mdpi.com/2306-5729/7/6/80feature fusionface recognitionLaplacian Pyramidmulti-resolution discrete cosine transform |
| spellingShingle | Bader M. AlFawwaz Atallah AL-Shatnawi Faisal Al-Saqqar Mohammad Nusir Multi-Resolution Discrete Cosine Transform Fusion Technique Face Recognition Model Data feature fusion face recognition Laplacian Pyramid multi-resolution discrete cosine transform |
| title | Multi-Resolution Discrete Cosine Transform Fusion Technique Face Recognition Model |
| title_full | Multi-Resolution Discrete Cosine Transform Fusion Technique Face Recognition Model |
| title_fullStr | Multi-Resolution Discrete Cosine Transform Fusion Technique Face Recognition Model |
| title_full_unstemmed | Multi-Resolution Discrete Cosine Transform Fusion Technique Face Recognition Model |
| title_short | Multi-Resolution Discrete Cosine Transform Fusion Technique Face Recognition Model |
| title_sort | multi resolution discrete cosine transform fusion technique face recognition model |
| topic | feature fusion face recognition Laplacian Pyramid multi-resolution discrete cosine transform |
| url | https://www.mdpi.com/2306-5729/7/6/80 |
| work_keys_str_mv | AT badermalfawwaz multiresolutiondiscretecosinetransformfusiontechniquefacerecognitionmodel AT atallahalshatnawi multiresolutiondiscretecosinetransformfusiontechniquefacerecognitionmodel AT faisalalsaqqar multiresolutiondiscretecosinetransformfusiontechniquefacerecognitionmodel AT mohammadnusir multiresolutiondiscretecosinetransformfusiontechniquefacerecognitionmodel |